Peace through Health and Medical Education: First Steps in Inclination of Healthcare Workers Toward Conflict-Preventive Activities
Bibliographic record
Abstract
BACKGROUND: The number of deaths and disabilities due to all types of violence has increased; violence and especially war heavily affect public and individual health and all sectors, including the health sector, are responsible for making attempts to take part in mitigation of war effects. However, "peace through health" has not been so far included globally in the curriculum of basic medical schools. The study aims to prepare data on responsibilities that could be devolved to health sector, and the importance and role of education for those health workers who are willing to participate in the peace field. METHODS: A systematic search in Web of Science, PubMed, Scopus and ERIC was conducted looking for relevant documents following combination of the key terms: peace, health and education. RESULTS: Health professionals consider war as a serious contagious disease that needs to be prevented like any other diseases. Prevention maneuvers at the primordial, primary, secondary and tertiary stages are important tasks that can be carried out by health professionals; there is an increasing demand for establishment of some courses; the roles and the manner of performing these tasks are not part of medical curriculum and for better execution of these roles, peace through health courses should be developed and then integrated to the current curriculum of health-related universities. CONCLUSION: The work of developing peace through health courses has been started before and it will continue until it completely becomes an accepted global course.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".